f-information measures in medical image registration
f-information measures in medical image registration
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DOI：
10.1117/12.431132
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发表时间：
2001-07
影响因子：
10.6
通讯作者：
J. Pluim;J. Maintz;Max A. Viergever
中科院分区：
文献类型：
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作者：
J. Pluim;J. Maintz;Max A. Viergever
目前备受关注的医学图像配准方法是互信息。该方法来源于信息理论，但在图像配准方面也被证明是成功的。然而，信息论提供了更多可能适用于图像配准的方法。这些都测量图像灰度值的联合分布与图像完全独立时发现的联合分布的散度。本文比较了互信息作为配准测度与其他f信息测度的性能。这些方法分别应用于正电子发射断层扫描(PET)/磁共振（MR）和磁共振(MR) /计算机断层扫描（CT）图像的刚性配准，分别对35对和41对图像进行配准。基于植入的标记物，可以对图像进行精确的金标准转换。研究了该方法的配准性能、鲁棒性和准确性。有些措施在各方面都表现不佳。大多数测量产生的结果与互信息的结果相似。然而，一个重要的发现是，有几种方法，虽然稍微难以优化，但可能比相互信息产生更准确的结果。
A measure for registration of medical images that currently draws much attention is mutual information. The measure originates from information theory, but has been shown to be successful for image registration as well. Information theory, however, offers many more measures that may be suitable for image registration. These all measure the divergence of the joint distribution of the images' grey values from the joint distribution that would have been found had the images been completely independent. This paper compares the performance of mutual information as a registration measure with that of other f-information measures. The measures are applied to rigid registration of positron emission tomography(PET)/magnetic resonance (MR) and MR/computed tomography (CT) images, for 35 and 41 image pairs, respectively. An accurate gold standard transformation is available for the images, based on implanted markers. The registration performance, robustness and accuracy of the measures are studied. Some of the measures are shown to perform poorly on all aspects. The majority of measures produces results similar to those of mutual information. An important finding, however, is that several measures, although slightly more difficult to optimize, can potentially yield significantly more accurate results than mutual information.